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Peer-reviewed veterinary case report

Use of machine learning and Poincaré density grid in the diagnosis of sinus node dysfunction caused by sinoatrial conduction block in dogs.

Journal:
Journal of veterinary internal medicine
Year:
2024
Authors:
Flanders, Wyatt Hutson et al.
Affiliation:
Department of Clinical Sciences · United States
Species:
dog

Abstract

BACKGROUND: Sinus node dysfunction because of abnormal impulse generation or sinoatrial conduction block causes bradycardia that can be difficult to differentiate from high parasympathetic/low sympathetic modulation (HP/LSM). HYPOTHESIS: Beat-to-beat relationships of sinus node dysfunction are quantifiably distinguishable by Poincar&#xe9; plots, machine learning, and 3-dimensional density grid analysis. Moreover, computer modeling establishes sinoatrial conduction block as a mechanism. ANIMALS: Three groups of dogs were studied with a diagnosis of: (1) balanced autonomic modulation (n&#x2009;=&#x2009;26), (2) HP/LSM (n&#x2009;=&#x2009;26), and (3) sinus node dysfunction (n&#x2009;=&#x2009;21). METHODS: Heart rate parameters and Poincar&#xe9; plot data were determined [median (25%-75%)]. Recordings were randomly assigned to training or testing. Supervised machine learning of the training data was evaluated with the testing data. The computer model included impulse rate, exit block probability, and HP/LSM. RESULTS: Confusion matrices illustrated the effectiveness in diagnosing by both machine learning and Poincar&#xe9; density grid. Sinus pauses >2&#x2009;s differentiated (P&#x2009;<&#x2009;.0001) HP/LSM (2340; 583-3947&#x2009;s) from sinus node dysfunction (8503; 7078-10&#x2009;050&#x2009;s), but average heart rate did not. The shortest linear intervals were longer with sinus node dysfunction (315; 278-323&#x2009;ms) vs HP/LSM (260; 251-292&#x2009;ms; P&#x2009;=&#x2009;.008), but the longest linear intervals were shorter with sinus node dysfunction (620; 565-698&#x2009;ms) vs HP/LSM (843; 799-888&#x2009;ms; P&#x2009;<&#x2009;.0001). CONCLUSIONS: Number and duration of pauses, not heart rate, differentiated sinus node dysfunction from HP/LSM. Machine learning and Poincar&#xe9; density grid can accurately identify sinus node dysfunction. Computer modeling supports sinoatrial conduction block as a mechanism of sinus node dysfunction.

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Original publication: https://pubmed.ncbi.nlm.nih.gov/38682817/